The band structure of BeTe - a combined experimental and theoretical study

Using angle-resolved synchrotron-radiation photoemission spectroscopy we have determined the dispersion of the valence bands of BeTe(100) along $\Gamma X$, i.e. the [100] direction. The measurements are analyzed with the aid of a first-principles calculation of the BeTe bulk band structure as well as of the photoemission peaks as given by the momentum conserving bulk transitions. Taking the calculated unoccupied bands as final states of the photoemission process, we obtain an excellent agreement between experimental and calculated spectra and a clear interpretation of almost all measured bands. In contrast, the free electron approximation for the final states fails to describe the BeTe bulk band structure along $\Gamma X$ properly.Comment: 21 pages plus 4 figure

High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research

Context-dependent effects on spatial variation in deer-vehicle collisions

Identifying factors that contribute to the risk of wildlife‐vehicle collisions (WVCs) has been a key focus of wildlife managers, transportation safety planners and road ecologists for over three decades. Despite these efforts, few generalities have emerged which can help predict the occurrence of WVCs, heightening the uncertainty under which conservation, wildlife and transportation management decisions are made. Undermining this general understanding is the use of study area boundaries that are incongruent with major biophysical gradients, inconsistent data collection protocols among study areas and species‐specific interactions with roads. We tested the extent to which factors predicting the occurrence of deer‐vehicle collisions (DVCs) were general among five study areas distributed over a 11,400‐km2 region in the Canadian Rocky Mountains. In spite of our system‐wide focus on the same genus (i.e., Odocoileus hemionus and O. virginianus), study area delineation along major biophysical gradients, and use of consistent data collection protocols, we found that large‐scale biophysical processes influence the effect of localized factors. At the local scale, factors predicting WVC occurrence varied greatly between individual study areas. Distance to water was an important predictor of WVCs in three of the five study areas, while other variables had modest importance in only two of the five study areas. Thus, lack of generality in factors predicting WVCs may have less to do with methodological or taxonomic differences among study areas than the large‐scale, biophysical context within which the data were collected. These results highlight the critical need to develop a conceptual framework in road ecology that can unify the disparate results emerging from field studies on WVC occurrence

Deterministic Classifiers Accuracy Optimization for Cancer Microarray Data

The objective of this study was to improve classification accuracy in cancer microarray gene expression data using a collection of machine learning algorithms available in WEKA. State of the art deterministic classification methods, such as: Kernel Logistic Regression, Support Vector Machine, Stochastic Gradient Descent and Logistic Model Trees were applied on publicly available cancer microarray datasets aiming to discover regularities that provide insights to help characterization and diagnosis correctness on each cancer typology. The implemented models, relying on 10-fold cross-validation, parameterized to enhance accuracy, reached accuracy above 90%. Moreover, although the variety of methodologies, no significant statistic differences were registered between them, at significance level 0.05, confirming that all the selected methods are effective for this type of analysis.info:eu-repo/semantics/publishedVersio

Polar body array CGH for prediction of the status of the corresponding oocyte. Part I: clinical results

Several randomized controlled trials have not shown a benefit from preimplantation genetic screening (PGS) biopsy of cleavage-stage embryos and assessment of up to 10 chromosomes for aneuploidy. Therefore, a proof-of-principle study was planned to determine the reliability of alternative form of PGS, i.e. PGS by polar body (PB) biopsy, with whole genome amplification and microarray-based comparative genomic hybridization (array CGH) analysis. In two centres, all mature metaphase II oocytes from patients who consented to the study were fertilized by ICSI. The first and second PBs (PB1and PB2) were biopsied and analysed separately for chromosome copy number by array CGH. If either or both of the PBs were found to be aneuploid, the corresponding zygote was then also processed by array CGH for concordance analysis. Both PBs were biopsied from a total of 226 zygotes from 42 cycles (average 5.5 per cycle; range 1-15) in 41 couples with an average maternal age of 40.0 years. Of these, the ploidy status of the zygote could be predicted in 195 (86%): 55 were euploid (28%) and 140 were aneuploid (72%). With only one exception, there was at least one predicted aneuploid zygote in each cycle and in 19 out of 42 cycles (45%), all zygotes were predicted to be aneuploid. Fresh embryos were transferred in the remaining 23 cycles (55%), and one frozen transfer was done. Eight patients had a clinical pregnancy of which seven were evolutive (ongoing pregnancy rates: 17% per cycle and 30% per transfer). The ploidy status of 156 zygotes was successfully analysed by array CGH: 38 (24%) were euploid and 118 (76%) were aneuploid. In 138 cases complete information was available on both PBs and the corresponding zygotes. In 130 (94%), the ploidy status of the zygote was concordant with the ploidy status of the PBs and in 8 (6%), the results were discordant. This proof-of-principle study indicates that the ploidy of the zygote can be predicted with acceptable accuracy by array CGH analysis of both PB

Genome-Wide DNA Methylation Profiling in Early Stage I Lung Adenocarcinoma Reveals Predictive Aberrant Methylation in the Promoter Region of the Long Noncoding RNA PLUT: An Exploratory Study

Introduction: Surgical procedure is the treatment of choice in early stage I lung adenocarcinoma. However, a considerable number of patients experience recurrence within the first 2 years after complete resection. Suitable prognostic biomarkers that identify patients at high risk of recurrence (who may probably benefit from adjuvant treatment) are still not available. This study aimed at identifying methylation markers for early recurrence that may become important tools for the development of new treatment modalities. Methods: Genome-wide DNA methylation profiling was performed on 30 stage I lung adenocarcinomas, comparing 14 patients with early metastatic recurrence with 16 patients with a long-term relapse-free survival period using methylated-CpG-immunoprecipitation followed by high-throughput next-generation sequencing. The differentially methylated regions between the two subgroups were validated for their prognostic value in two independent cohorts using the MassCLEAVE assay, a high-resolution quantitative methylation analysis. Results: Unsupervised clustering of patients in the discovery cohort on the basis of differentially methylated regions identified patients with shorter relapse-free survival (hazard ratio: 2.23; 95% confidence interval: 0.66-7.53; p = 0.03). In two validation cohorts, promoter hypermethylation of the long noncoding RNA PLUT was significantly associated with shorter relapse-free survival (hazard ratio: 0.54; 95% confidence interval: 0.31-0.93; p < 0.026) and could be reported as an independent prognostic factor in the multivariate Cox regression analysis. Conclusions: Promoter hypermethylation of the long noncoding RNA PLUT is predictive in patients with early stage I adenocarcinoma at high risk for early recurrence. Further studies are needed to validate its role in carcinogenesis and its use as a biomarker to facilitate patient selection and risk stratification

The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

status: publishe

Use of machine learning to shorten observation-based screening and diagnosis of autism

The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization—in particular those focused on assessment of short home videos of children—that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk

Optimizing humanitarian aids : formulating influencer advertisement in social networks

In order to solve problems encountered during natural disasters, in addition to NGOs and relief teams, various individuals intend to help the injured. Although the cooperation of people has remarkable advantages, the disparity between the needs of the injured and the people’s donations can cause problems such as trouble for relief teams and wasting the substantial resources. In generic, the influencer selection in the marketing endeavors is mainly aimed to maximize people’s awareness and attention, but this research proposes a method for influencer selection, using Social Network Analysis (SNA) and optimization techniques, by which it is possible to establish an adaptation between the public attention and the type of injured necessities. The proposed method is applied to a real sample network of Facebook friends, to evaluate the efficiency and validity of the formulated method